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Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
Published on: July 12, 2018
A cautionary tale of batch corrections on confounded microbiome community profiles
Alicia J Foxx1,2,3, Adam R Rivers4
1Department of Plant Biology and Conservation Northwestern University Evanston Illinois USA.
None:
We use a case study of seed microbiomes to assess the performance of five batch effects correction algorithms (BECAs) (zero-mean centering (ZMC), Ratio-A, conditional quantile regression (ConQuR), partial least squares discriminant analysis (PLSDA), and weighted PLSDA on a confounded data set in which the covariate of interest (plant species) does not appear in all batches (studies). We show incomplete batch effects removal in all cases, which calls for careful application of and future work on BECAs.
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